Correlated outcomes commonly occur in biomedical and health service research. Examples include repeated measurement outcomes and clustered outcomes. Correlations among observations must be taken into consideration in sample size calculations due to potential associations for observations in clustered and longitudinal studies. Sample size determination has to also incorporate missing data in the repeated measurements studies and unequal cluster sizes in clustered outcomes studies. I present parametric and nonparametric methods to calculate sample sizes and powers for studies with correlated outcomes. Design and analysis of correlated outcomes will be illustrated using examples from studies of diagnostic sensitivity and specificity, community intervention studies and repeated measurement studies.